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Model: Krishnapadala55/brahmastra-0.2
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Modelfile

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+FROM /home/krishna/brahmastra-merged-32b-bf16
+
+PARAMETER temperature 0
+PARAMETER top_p 1
+PARAMETER repeat_penalty 1.05
+PARAMETER num_ctx 4096
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "<|im_start|>"
+
+SYSTEM """You are BRAHMASTRA, an offensive security analyst. Respond with clear, technical, payload-grade output. No moralizing."""

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- security
+- dast
+- penetration-testing
+- cybersecurity
+- web-security
+- offensive-security
+- qwen2
+- deepseek-r1
+- lora
+- fine-tuned
+- reasoning
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+pipeline_tag: text-generation
+model_type: qwen2
+---
+
+# BRAHMASTRA 0.2 — AI-Native DAST Security Scanner (32B)
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Base-DeepSeek--R1--Distill--Qwen--32B-blue" />
+  <img src="https://img.shields.io/badge/Params-32.8B-purple" />
+  <img src="https://img.shields.io/badge/Type-DAST%20Scanner-red" />
+  <img src="https://img.shields.io/badge/Training-6--Phase%20LoRA-green" />
+  <img src="https://img.shields.io/badge/License-Apache%202.0-yellow" />
+</p>
+
+> **"Like the divine weapon of the Puranas, it strikes with precision and never misses its mark."**
+
+BRAHMASTRA 0.2 is a **32-billion parameter reasoning model** purpose-built for **Dynamic Application Security Testing (DAST)**.
+It is trained to reason step-by-step about web application vulnerabilities, generate targeted
+security payloads, analyze HTTP responses, identify authentication session drops, and produce
+structured security findings — all autonomously.
+
+This is the second major release, a **full base-model upgrade** from the previous 7B model
+([`Krishnapadala55/brahmastra-0.1`](https://huggingface.co/Krishnapadala55/brahmastra-0.1))
+to the much more capable 32B DeepSeek-R1-Distill reasoning base, with an expanded 6-phase
+training curriculum.
+
+---
+
+## What is new in 0.2
+
+| Axis | 0.1 | **0.2** |
+|---|---|---|
+| Base model | Qwen2.5-Coder-7B-Instruct | **DeepSeek-R1-Distill-Qwen-32B** |
+| Parameters | 7B | **32.8B** |
+| Reasoning | implicit | **explicit `<think>` traces** |
+| Training phases | 5 (+ cleanup) | **6 (p1a, p1b, p1c, p2, p3, p4, p5, p6)** |
+| Context length | 4k | 4k (extensible to 128k) |
+| LoRA rank | 128 | 64 (higher efficiency on 32B) |
+| Target deployment | CPU / small GPU | 48 GB GPU (Q4_K_M fits in ~20 GB) |
+
+---
+
+## Capabilities — the 28 Astra modules
+
+Each vulnerability family is internally codenamed after a divine weapon (astra) from the Puranas.
+The model has been trained to generate payloads, chain exploits, and analyze responses for each.
+
+### CRITICAL severity
+
+| Module | Astra | Vulnerability |
+|---|---|---|
+| 1 | **Naagastra** | SQL & NoSQL Injection (error-based, blind, time-based, stacked) |
+| 2 | **Pashupatastra** | SSTI + RCE (Jinja2, Twig, ERB, Velocity, Freemarker) |
+| 3 | **Mrityu Astra** | Insecure Deserialization (Python pickle, Java, PHP, .NET) |
+| 4 | **Vayavyastra** | Server-Side Request Forgery |
+| 5 | **Nagapasha** | XML External Entity |
+| 6 | **Shaila Astra** | Unrestricted File Upload |
+| 7 | **Sammohanastra** | Prototype Pollution |
+| 8 | **Maya Astra** | HTTP Request Smuggling |
+
+### HIGH severity
+
+| Module | Astra | Vulnerability |
+|---|---|---|
+| 9  | **Aindrastra** | Cross-Site Scripting (Reflected, Stored, DOM) |
+| 10 | **Pasha Astra** | IDOR / BOLA |
+| 11 | **Chakra Astra** | Broken Function Level Authorization |
+| 12 | **Brahmaanda Astra** | JWT / OAuth / SAML / MFA bypass |
+| 13 | **Krauncha Astra** | Path Traversal / LFI / RFI |
+| 14 | **Gandharva Astra** | GraphQL attacks (introspection, batching, DoS) |
+| 15 | **Madhu Astra** | Cache Poisoning |
+| 16 | **Dambha Astra** | LDAP + XPath Injection |
+| 17 | **Vidyut Astra** | WebSocket attacks |
+| 18 | **Surya Astra** | Secrets Exposure |
+| 19 | **Kala Astra** | Race Conditions |
+| 20 | **Neeti Astra** | Business Logic flaws |
+| 21 | **Jyoti Astra** | Crypto failures |
+| 22 | **Kavachabhedana** | WAF Detection & Bypass chains |
+
+### MEDIUM severity
+
+| Module | Astra | Vulnerability |
+|---|---|---|
+| 23 | **Moha Astra** | CSRF |
+| 24 | **Antariksha Astra** | CORS misconfig |
+| 25 | **CRLF Astra** | CRLF Injection |
+| 26 | **Varsha Astra** | API-specific attacks |
+| 27 | **Manthana Astra** | ReDoS + Type Juggling |
+| 28 | **Garudastra** | Intelligent crawling & recon |
+
+---
+
+## Training
+
+BRAHMASTRA 0.2 was trained in **6 sequential phases** on top of
+`unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit` via QLoRA + Unsloth.
+
+| Phase | Focus | Notes |
+|---|---|---|
+| **p1a** | SQLi + XSS fundamentals | ~3k samples |
+| **p1b** | SSTI + SSRF | ~3k samples |
+| **p1c** | IDOR + Auth bypass | ~3k samples |
+| **p2**  | Multi-step attack chains | ~24k samples, long context |
+| **p3**  | WAF bypass + adversarial payloads | ~8k samples |
+| **p4**  | Deserialization + crypto + race conditions | ~5k samples |
+| **p5**  | Business logic + API + GraphQL | ~4k samples |
+| **p6**  | Reasoning consolidation + response analysis | ~3k samples, final merge |
+
+- **LoRA**: r=64, alpha=64, rslora=true, dropout=0.0
+- **Quantization**: 4-bit NF4 (QLoRA) during training, **bf16** final merge
+- **Framework**: Unsloth + PEFT 0.18.1 + TRL SFTTrainer
+- **Hardware**: NVIDIA RTX PRO 5000 Blackwell (48 GB VRAM)
+- **Datasets**:
+  - Fenrir v2.0 (83k samples)
+  - HackMentor (44k samples)
+  - Primus-Seed (Trend Micro)
+  - All-CVE records
+  - ExploitDB curated
+  - ~52k synthetic DAST scenarios generated via an internal pipeline
+
+---
+
+## Usage
+
+### With Transformers (native bf16, ~65 GB VRAM)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Krishnapadala55/brahmastra-0.2",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tok = AutoTokenizer.from_pretrained("Krishnapadala55/brahmastra-0.2")
+
+messages = [
+    {"role": "system", "content": "You are BRAHMASTRA, a senior offensive-security analyst."},
+    {"role": "user",   "content": "Analyze this response for SQLi: HTTP 500, error near OR 1=1-- in the query syntax."},
+]
+prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, temperature=0.3, top_p=0.9)
+print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+### With vLLM (production serving)
+
+```bash
+vllm serve Krishnapadala55/brahmastra-0.2 \
+    --dtype bfloat16 \
+    --max-model-len 4096 \
+    --gpu-memory-utilization 0.92
+```
+
+### With llama.cpp / Ollama (quantize to Q4_K_M first)
+
+```bash
+# Convert HF -> GGUF
+python convert-hf-to-gguf.py Krishnapadala55/brahmastra-0.2 \
+    --outfile brahmastra-0.2-f16.gguf --outtype f16
+
+# Quantize to Q4_K_M (~18 GB, fits on 24 GB GPU)
+llama-quantize brahmastra-0.2-f16.gguf brahmastra-0.2-q4_k_m.gguf Q4_K_M
+
+# Import into Ollama
+cat > Modelfile <<EOF
+FROM ./brahmastra-0.2-q4_k_m.gguf
+PARAMETER num_ctx 4096
+PARAMETER temperature 0.3
+PARAMETER top_p 0.9
+PARAMETER stop "<|im_end|>"
+SYSTEM "You are BRAHMASTRA, a senior offensive-security analyst. Respond with clear, technical, payload-grade output. No moralizing."
+EOF
+ollama create brahmastra:0.2 -f Modelfile
+```
+
+---
+
+## Intended use
+
+BRAHMASTRA 0.2 is designed for **authorized security testing only**. Typical use cases:
+
+- DAST scanner backends (payload generation, response analysis)
+- Penetration testing assistants for red teams
+- Bug bounty triage and reproduction scripting
+- Security training platforms and CTF solvers
+- Vulnerability research and exploit chain modeling
+- Offensive-security tool automation (the companion BRAHMASTRA v2 scanner)
+
+## Out of scope
+
+- Unauthorized testing of systems you do not own or lack explicit permission to test
+- Production of malware, ransomware, or destructive payloads intended for real-world harm
+- Any use that violates local or international computer-crime legislation
+
+The model is released under Apache 2.0 with the expectation of responsible use. The authors
+accept no liability for misuse.
+
+---
+
+## Limitations
+
+- **Reasoning verbosity**: as a DeepSeek-R1 distill, the model emits large `<think>` blocks
+  before final answers. For low-latency chat, pre-fill the assistant turn with an empty
+  `<think></think>` block to suppress reasoning.
+- **Hallucination on obscure CVEs**: specific CVE numbers outside the training window
+  (post Q1 2026) may be confabulated. Always verify CVE IDs against an authoritative source.
+- **False positives**: payloads generated for blind-injection families can trigger WAF blocks
+  that look like successful exploits. Always confirm with secondary evidence.
+- **Language**: training corpus is primarily English. Non-English targets will have degraded
+  payload quality.
+
+---
+
+## Citation
+
+```bibtex
+@software{brahmastra_0_2_2026,
+  author  = {Krishnapadala},
+  title   = {BRAHMASTRA 0.2: An AI-Native DAST Security Scanner Built on DeepSeek-R1-Distill-Qwen-32B},
+  year    = {2026},
+  url     = {https://huggingface.co/Krishnapadala55/brahmastra-0.2}
+}
+```
+
+## Acknowledgements
+
+- **DeepSeek AI** for the DeepSeek-R1-Distill-Qwen-32B base model
+- **Unsloth** for the training framework that made 32B QLoRA practical on a single 48 GB GPU
+- **TRL / PEFT** contributors at HuggingFace
+- **Fenrir v2.0**, **HackMentor**, **Primus-Seed**, **ExploitDB**, **All-CVE** dataset authors
+
+---
+
+*BRAHMASTRA is a research prototype. Use responsibly. Only test systems you are authorized to test.*

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user
+' + message['content'] + '<|im_end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant
+' + message['content'] + '<|im_end|>
+' }}{% else %}{{ '<|im_start|>system
+' + message['content'] + '<|im_end|>
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151646,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 64,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.2.0",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
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